System and method for multitask processing for autonomous vehicle computation and control

ABSTRACT

A system and method for multitask processing for autonomous vehicle computation and control includes: receiving training image data from a training image data collection system; performing a training phase to train a plurality of tasks associated with features of the training image data, the training phase including extracting common features from the training image data, causing the plurality of tasks to generate task-specific predictions based on the training image data, determining a bias between the task-specific prediction for each task and corresponding task-specific ground truth data, and adjusting parameters of each of the plurality of tasks to cause the bias to meet a pre-defined confidence level; receiving image data from an image data collection system associated with an autonomous vehicle; and performing an operational phase including extracting common features from the image data, causing the plurality of trained tasks to concurrently generate task-specific predictions based on the image data.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the U.S. Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. The following notice applies to the disclosure herein and to the drawings that form a part of this document: Copyright 2016-2017, TuSimple, All Rights Reserved.

TECHNICAL FIELD

This patent document pertains generally to tools (systems, apparatuses, methodologies, computer program products, etc.) for vehicle control systems, and autonomous driving systems, route planning, trajectory planning, image processing, and more particularly, but not by way of limitation, to a system and method for multitask processing for autonomous vehicle computation and control.

BACKGROUND

Processing subsystems within autonomous vehicles typically include decision making subsystems, trajectory planning, image processing, and control operations, among other subsystems. These autonomous vehicle processing subsystems are responsible for receiving a substantial amount of sensor data and other input and for accurately processing this input data in real time. The processing loads can be very high and the available processing time is very short. The safety and efficiency of the autonomous vehicle and its occupants depend on the ability of these autonomous vehicle processing subsystems to perform as needed. It is certainly possible to configure an autonomous vehicle control system with high-powered and expensive data processing systems that will handle the processing loads. However, there is constant pressure in the marketplace to design and build autonomous vehicle control systems with lowest cost, lightest weight, lowest power requirements, lowest operating temperatures, and high levels of adaptability and customization. Conventional autonomous vehicle control systems have been unable to meet this challenge while providing responsive, reliable, and efficient autonomous vehicle control.

SUMMARY

A system and method for multitask processing for autonomous vehicle computation and control are disclosed herein. Specifically, the present disclosure relates to systems, methods, and devices that facilitate the image processing, decision making, and control processes in an autonomous driving mode. In an example embodiment, an autonomous vehicle computation and control system can be configured to determine the intrinsic similarity of features in image or perception data received from the sensors or image capture devices of an autonomous vehicle. The similar or shared features can have corresponding tasks that can be configured to execute concurrently to save task-specific execution time and achieve higher data processing speeds. In example embodiments, a portion of the computation load, represented by the multiple tasks of the shared features, can be associated with shared layers among different pixel-level segmentation. The multiple tasks in these shared layers can be configured to execute concurrently, thereby increasing processing parallelism and decreasing aggregate execution time.

BRIEF DESCRIPTION OF THE DRAWINGS

The various embodiments are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which:

FIG. 1 illustrates a first phase for training an autonomous vehicle computation and control system in an example embodiment;

FIG. 2 illustrates a detail of the first phase for training the autonomous vehicle computation and control system in an example embodiment;

FIG. 3 illustrates a second phase for operational or simulation use of the autonomous vehicle computation and control system in an example embodiment;

FIG. 4 illustrates a detail of the second phase for operational or simulation use of the autonomous vehicle computation and control system in an example embodiment;

FIG. 5 is a process flow diagram illustrating an example embodiment of a system and method for multitask processing for autonomous vehicle computation and control; and

FIG. 6 shows a diagrammatic representation of machine in the example form of a computer system within which a set of instructions when executed may cause the machine to perform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the various embodiments. It will be evident, however, to one of ordinary skill in the art that the various embodiments may be practiced without these specific details.

A system and method for multitask processing for autonomous vehicle computation and control are disclosed herein. Specifically, the present disclosure relates to systems, methods, and devices that facilitate the image processing, decision making, and control processes in an autonomous driving mode. In an example embodiment, an autonomous vehicle computation and control system can be configured to determine the intrinsic similarity of features in image or perception data received from the sensors or image capture devices of an autonomous vehicle. The similar or shared features can have corresponding tasks that can be configured to execute concurrently to save serial task-specific execution time and achieve higher data processing speeds. In example embodiments, a portion of the computational load, represented by the multiple tasks of the shared features, can be associated with shared layers among different pixel-level image segmentation. The multiple tasks in these shared layers can be configured to execute concurrently, thereby increasing processing parallelism and decreasing aggregate execution time.

As described in various example embodiments, a system and method for multitask processing for autonomous vehicle computation and control are described herein. Referring to FIG. 1, an example embodiment disclosed herein can be used in the context of an autonomous vehicle computation and control system 210 for autonomous vehicles. FIG. 1 illustrates an offline training phase (a first phase) used to configure or train the autonomous vehicle computation and control system 210 in an example embodiment based on training image data 201 and manually annotated image data 203 representing ground truth. In the example embodiment, a training image data collection system 201 can be used gather perception data to train or configure processing parameters for the autonomous vehicle computation and control system 210 with training image data. As described in more detail below for an example embodiment, after the initial training phase, the autonomous vehicle computation and control system 210 can be used in an operational or simulation phase (a second phase) to generate image feature predictions based on image data received by the autonomous vehicle computation and control system 210 and based on the training the autonomous vehicle computation and control system 210 receives during the initial offline training phase.

Referring again to FIG. 1, the training image data collection system 201 can include an array of perception information gathering devices or sensors that may include image generating devices (e.g., cameras), light amplification by stimulated emission of radiation (laser) devices, light detection and ranging (LIDAR) devices, global positioning system (GPS) devices, sound navigation and ranging (sonar) devices, radio detection and ranging (radar) devices, and the like. The perception information gathered by the information gathering devices at various traffic locations can include traffic or vehicle image data, roadway data, environmental data, distance data from LIDAR or radar devices, and other sensor information received from the information gathering devices of the data collection system 201 positioned adjacent to particular roadways (e.g., monitored locations). Additionally, the data collection system 201 can include information gathering devices installed in moving test vehicles being navigated through pre-defined routings in an environment or location of interest. Some portions of the ground truth data can also be gathered by the data collection system 201.

The image data collection system 201 can collect actual trajectories of vehicles, moving or static objects, roadway features, environmental features, and corresponding ground truth data under different scenarios. The different scenarios can correspond to different locations, different traffic patterns, different environmental conditions, and the like. The image data and other perception data and ground truth data collected by the data collection system 201 reflects truly realistic, real-world traffic information related to the locations or routings, the scenarios, and the vehicles or objects being monitored. Using the standard capabilities of well-known data collection devices, the gathered traffic and vehicle image data and other perception or sensor data can be wirelessly transferred (or otherwise transferred) to a data processor of a standard computing system, upon which the image data collection system 201 can be executed. Alternatively, the gathered traffic and vehicle image data and other perception or sensor data can be stored in a memory device at the monitored location or in the test vehicle and transferred later to the data processor of the standard computing system.

As shown in FIG. 1, a manual annotation data collection system 203 is provided to apply labels to features found in the training images collected by the data collection system 201. These training images can be analyzed by human labelers or automated processes to manually define labels or classifications for each of the features identified in the training images. As such, the manually annotated image labels can represent the ground truth data corresponding to the training images from the image data collection system 201. These feature labels or ground truth data can be provided to the autonomous vehicle computation and control system 210 as part of the offline training phase as described in more detail below.

The traffic and vehicle image data and other perception or sensor data for training, the feature label data, and the ground truth data gathered or calculated by the training image data collection system 201 and the object or feature labels produced by the manual annotation data collection system 203 can be used to generate training data, which can be processed by the autonomous vehicle computation and control system 210 in the offline training phase. For example, as well-known, neural networks can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase. As described in more detail below, the training data provided by the image data collection system 201 and the manual annotation data collection system 203 can be used to train the autonomous vehicle computation and control system 210 to configure a set of tasks corresponding to the features identified in the training images and to enable multitask concurrent execution of tasks based on commonalities of the identified features. The offline training phase of the autonomous vehicle computation and control system 210 is described in more detail below.

Referring now to FIG. 2, a detail of the first phase for offline training of the autonomous vehicle computation and control system 210 in an example embodiment is illustrated. In a first operational block 310 shown in FIG. 2, images are collected and provided to the autonomous vehicle computation and control system 210 by the training image data collection system 201. As described above, the images can be training images, real world images, simulated images, or other images suitable for training the machine learning components of the autonomous vehicle computation and control system 210. The images collected in block 310 can also be provided to the manual annotation data collection system 203, where the images can be manually annotated or labeled by human labelers or automated processes. The manually annotated image labels can represent the ground truth data corresponding to the training images from the training image data collection system 201.

As shown in FIG. 2, the training image data collected in block 310 can be provided to a processing block 314 of the autonomous vehicle computation and control system 210, where the features of the image data can be extracted. Semantic segmentation or similar processes can be used for the feature extraction. As well-known, feature extraction can provide a pixel-level object label for each feature identified in the image data. In many cases, the image data will contain features with common characteristics. The autonomous vehicle computation and control system 210 can associate a plurality of tasks with the features identified in the image data. Alternatively, the autonomous vehicle computation and control system 210 can use a plurality of tasks to identify features or feature characteristics in the image data. During the offline training phase, these multiple tasks can be trained to predict features of feature characteristics by using the extracted feature data and computing task-specific predictions of feature characteristics in block 316. Each task of the multiple tasks can execute concurrently to produce a prediction for the corresponding task as shown in FIG. 2. These task-specific predictions of features or feature characteristics in the image data can be compared with corresponding ground truth data obtained from the manual annotation data collection system 203. Each task prediction of the multiple task predictions can be associated with the corresponding ground truth data as a task-specific label as shown in FIG. 2. Given the task-specific predictions and the corresponding task-specific ground truth, the autonomous vehicle computation and control system 210 can compute loss values (blocks 318) corresponding to the difference between the task-specific prediction for each task and the corresponding task-specific ground truth. As a result, the autonomous vehicle computation and control system 210 can generate a bias for each task corresponding to the difference between a set of predicted data and a set of labeled data based on the image data received from the cameras. This bias for each task can be used to adjust parameters in block 316 for each task to reduce the difference between the task-specific prediction for each task and the corresponding task-specific ground truth. With repeated iterations and a sufficient quantity of input training image data, the task-specific predictions will converge toward the corresponding task-specific ground truth.

In most cases, some tasks of the multiple tasks will converge more quickly than other tasks. In an example embodiment, each task can have an associated weight or weighted value that corresponds to the degree of confidence that the task is producing sufficiently accurate prediction data. The weighted value can correspond to the task biases described above or the task parameters adjusted to reduce the difference between the task-specific prediction for each task and the corresponding task-specific ground truth. The example embodiment can also establish a pre-defined confidence level that corresponds to an acceptable level of accuracy for the predicted data produced by each task. At each iteration, the weighted value of the task can be compared with the pre-defined confidence level. If the weighted value for a particular task is higher than, greater than, or exceeds the pre-defined confidence level, the particular task is subject to the offline training process as described above. Once the weighted value for a particular task is lower than, less than, equal to, or does not exceed the pre-defined confidence level, the particular task is determined to be sufficiently trained and is no longer subject to the offline training process as described above. In this manner, each of the multiple tasks are trained only so long as each task is unable to meet the pre-defined confidence level. Thus, processing resources are conserved by not continuing to train tasks that have already reached an acceptable performance level. Eventually, all or most of the multiple tasks will reach the acceptable performance level defined by the pre-defined confidence level. At this point, the offline training process is complete and the parameters associated with each task have been properly adjusted to cause the task to produce sufficiently accurate predicted features, feature characteristics, or feature labels corresponding to the input image data. After being trained by the offline training process as described above, the multiple tasks with their properly adjusted parameters can be deployed in an operational or simulation phase as described below in connection with FIGS. 3 and 4.

FIG. 3 illustrates a second phase for operational or simulation use of the autonomous vehicle computation and control system 210 in an example embodiment. As shown in FIG. 3, the autonomous vehicle computation and control system 210 can receive image data from the image data collection system 205. The image data collection system 205 can include an array of perception information gathering devices, sensors, and/or image generating devices on or associated with an autonomous vehicle, similar to the perception information gathering devices of the image data collection system 201. As described in more detail below, the autonomous vehicle computation and control system 210 can process the input image data with the plurality of trained tasks to produce image feature predictions 220, which can be used by other autonomous vehicle subsystems to configure or control the operation of the autonomous vehicle.

FIG. 4 illustrates a detail of the second phase for operational or simulation use of the autonomous vehicle computation and control system in an example embodiment. As described above, the multiple trained tasks with their properly adjusted parameters can be deployed in an operational or simulation phase. In a first operational block 410 shown in FIG. 4, images are collected and provided to the autonomous vehicle computation and control system 210 by the image data collection system 205. As described above, the images can be real world images, simulated images, or the like as collected from cameras, sensors, or other perception devices on or associated with an autonomous vehicle. As shown in FIG. 4, the image data collected in block 410 can be provided to a processing block 414 of the autonomous vehicle computation and control system 210, where the features of the image data can be extracted. Semantic segmentation or similar processes can be used for the feature extraction. As well-known, feature extraction can provide a pixel-level object label for each feature identified in the image data. In many cases, the image data will contain features with common characteristics. The autonomous vehicle computation and control system 210 can associate the multiple trained tasks with the features identified in the image data. Alternatively, the autonomous vehicle computation and control system 210 can use the multiple trained tasks to identify features or feature characteristics in the image data. Because of the offline training phase as described above, the multiple tasks have been trained to accurately predict features, feature characteristics, or feature labels in the input image data. In block 416, each task of the multiple trained tasks can execute concurrently to produce a prediction output for each of the corresponding tasks as shown in FIG. 4. As a result, a plurality of task-specific prediction outputs are produced in blocks 418. Because each of the multiple tasks were trained in the offline training phase as described above, the task-specific prediction outputs 418 produced in the operational or simulation phase are highly accurate. In fact, the task-specific prediction outputs are configured to be accurate to a level within the threshold established by the pre-defined confidence level as described above. Moreover, because the multiple trained tasks can be executed concurrently, the input image data can be processed very quickly, which is critical for operation in the real-time environment of autonomous vehicles. The autonomous vehicle computation and control system 210 can process the input image data with the plurality of trained tasks to produce image feature predictions 220, which can be used by other autonomous vehicle subsystems to configure or control the operation of the autonomous vehicle. Thus, a system and method for multitask processing for autonomous vehicle computation and control are disclosed.

Referring now to FIG. 5, a flow diagram illustrates an example embodiment of a system and method 1000 for multitask processing for autonomous vehicle computation and control. The example embodiment can be configured for: receiving training image data from a training image data collection system (processing block 1010); obtaining ground truth data corresponding to the training image data (processing block 1020); performing a training phase to train a plurality of tasks associated with features of the training image data, the training phase including extracting common features from the training image data, causing the plurality of tasks to generate task-specific predictions based on the training image data, determining a bias between the task-specific prediction for each task and corresponding task-specific ground truth data, and adjusting parameters of each of the plurality of tasks to cause the bias to meet a pre-defined confidence level (processing block 1030); receiving image data from an image data collection system associated with an autonomous vehicle (processing block 1040); and performing an operational phase including extracting common features from the image data, causing the plurality of trained tasks to concurrently generate task-specific predictions based on the image data, and output the task-specific predictions to an autonomous vehicle subsystem of the autonomous vehicle (processing block 1050).

FIG. 6 shows a diagrammatic representation of a machine in the example form of a computing system 700 within which a set of instructions when executed and/or processing logic when activated may cause the machine to perform any one or more of the methodologies described and/or claimed herein. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a laptop computer, a tablet computing system, a Personal Digital Assistant (PDA), a cellular telephone, a smartphone, a web appliance, a set-top box (STB), a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) or activating processing logic that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” can also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions or processing logic to perform any one or more of the methodologies described and/or claimed herein.

The example computing system 700 can include a data processor 702 (e.g., a System-on-a-Chip (SoC), general processing core, graphics core, and optionally other processing logic) and a memory 704, which can communicate with each other via a bus or other data transfer system 706. The mobile computing and/or communication system 700 may further include various input/output (I/O) devices and/or interfaces 710, such as a touchscreen display, an audio jack, a voice interface, and optionally a network interface 712. In an example embodiment, the network interface 712 can include one or more radio transceivers configured for compatibility with any one or more standard wireless and/or cellular protocols or access technologies (e.g., 2nd (2G), 2.5, 3rd (3G), 4th (4G) generation, and future generation radio access for cellular systems, Global System for Mobile communication (GSM), General Packet Radio Services (GPRS), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (WCDMA), LTE, CDMA2000, WLAN, Wireless Router (WR) mesh, and the like). Network interface 712 may also be configured for use with various other wired and/or wireless communication protocols, including TCP/IP, UDP, SIP, SMS, RTP, WAP, CDMA, TDMA, UMTS, UWB, WiFi, WiMax, Bluetooth™, IEEE 802.11x, and the like. In essence, network interface 712 may include or support virtually any wired and/or wireless communication and data processing mechanisms by which information/data may travel between a computing system 700 and another computing or communication system via network 714.

The memory 704 can represent a machine-readable medium on which is stored one or more sets of instructions, software, firmware, or other processing logic (e.g., logic 708) embodying any one or more of the methodologies or functions described and/or claimed herein. The logic 708, or a portion thereof, may also reside, completely or at least partially within the processor 702 during execution thereof by the mobile computing and/or communication system 700. As such, the memory 704 and the processor 702 may also constitute machine-readable media. The logic 708, or a portion thereof, may also be configured as processing logic or logic, at least a portion of which is partially implemented in hardware. The logic 708, or a portion thereof, may further be transmitted or received over a network 714 via the network interface 712. While the machine-readable medium of an example embodiment can be a single medium, the term “machine-readable medium” should be taken to include a single non-transitory medium or multiple non-transitory media (e.g., a centralized or distributed database, and/or associated caches and computing systems) that store the one or more sets of instructions. The term “machine-readable medium” can also be taken to include any non-transitory medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the various embodiments, or that is capable of storing, encoding or carrying data structures utilized by or associated with such a set of instructions. The term “machine-readable medium” can accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment. 

What is claimed is:
 1. A system comprising: a data processor; and a memory for storing an autonomous vehicle computation and control system, executable by the data processor, the autonomous vehicle computation and control system being configured to: receive training image data from a training image data collection system, the training image data collection system comprising sensors installed in a moving test vehicle navigated through real-world traffic scenarios, the training image data comprising data collected from real-world traffic scenarios; obtain ground truth data corresponding to the training image data, the ground truth data corresponding to the data collected from real-world traffic scenarios; perform a training phase to train a plurality of tasks each associated with different features of the training image data, at least one task of the plurality of tasks corresponding to a specific feature of the training image data, the plurality of tasks configured to execute concurrently, the training phase comprising extracting features from the training image data, associating similar extracted features with a corresponding task of the plurality of tasks, associating different extracted features with different other tasks of the plurality of tasks, causing the plurality of tasks to generate task-specific predictions of feature characteristics based on the training image data, determining a bias between the task-specific prediction for each task and corresponding task-specific ground truth data, and adjusting parameters of each of the plurality of tasks to cause the bias to meet a pre-defined confidence level; receive image data from an image data collection system associated with an autonomous vehicle; and perform an operational phase comprising extracting features from the image data, causing the plurality of trained tasks to execute concurrently to generate task-specific predictions of feature characteristics based on the image data, and output the task-specific predictions to an autonomous vehicle subsystem of the autonomous vehicle.
 2. The system of claim 1 being further configured to use machine learnable components.
 3. The system of claim 1 wherein the training image data collection system comprises a video camera and a light detection and ranging (LIDAR) device.
 4. The system of claim 1 wherein the system being configured to obtain ground truth data from a manual annotation data collection system, which comprises gathering information from human labelers.
 5. The system of claim 1 wherein the system being further configured to receive a plurality of training images gathered from a plurality of perception devices by the training image data collection system.
 6. The system of claim 1 wherein the system being further configured to receive a plurality of images gathered from a plurality of perception devices at various traffic locations by the image data collection system.
 7. The system of claim 1 wherein the system being further configured to provide highly accurate image feature predictions for configuring a control system in an autonomous vehicle.
 8. A method comprising: receiving training image data from a training image data collection system, the training image data collection system comprising sensors installed in a moving test vehicle navigated through real-world traffic scenarios, the training image data comprising data collected from real-world traffic scenarios; obtaining ground truth data corresponding to the training image data, the ground truth data corresponding to the data collected from real-world traffic scenarios; performing a training phase to train a plurality of tasks each associated with different features of the training image data, at least one task of the plurality of tasks corresponding to a specific feature of the training image data, the plurality of tasks configured to execute concurrently, the training phase comprising extracting features from the training image data, associating similar extracted features with a corresponding task of the plurality of tasks, associating different extracted features with different other tasks of the plurality of tasks, causing the plurality of tasks to generate task-specific predictions of feature characteristics based on the training image data, determining a bias between the task-specific prediction for each task and corresponding task-specific ground truth data, and adjusting parameters of each of the plurality of tasks to cause the bias to meet a pre-defined confidence level; receiving image data from an image data collection system associated with an autonomous vehicle; and performing an operational phase comprising extracting features from the image data, causing the plurality of trained tasks to execute concurrently to generate task-specific predictions of feature characteristics based on the image data, and output the task-specific predictions to an autonomous vehicle subsystem of the autonomous vehicle.
 9. The method of claim 8 further comprises further comprising extracting features to provide a pixel-level object label for each feature.
 10. The method of claim 8 wherein the determined bias comprises a computed loss value.
 11. The method of claim 8 further comprising obtaining ground truth data from an automated process.
 12. The method of claim 8 further comprising associating a weight to each of the plurality of tasks.
 13. The method of claim 8 further comprising continuing to train a task of the plurality of tasks while the bias fails to meet a pre-defined confidence level.
 14. The method of claim 8 wherein the image data collection system comprises perception devices, wherein the perception devices comprise cameras and sensors.
 15. A non-transitory machine-useable storage medium embodying instructions which, when executed by a machine, cause the machine to: receive training image data from a training image data collection system, the training image data collection system comprising sensors installed in a moving test vehicle navigated through real-world traffic scenarios, the training image data comprising data collected from real-world traffic scenarios; obtain ground truth data corresponding to the training image data, the ground truth data corresponding to the data collected from real-world traffic scenarios; perform a training phase to train a plurality of tasks each associated with different features of the training image data, at least one task of the plurality of tasks corresponding to a specific feature of the training image data, the plurality of tasks configured to execute concurrently, the training phase comprising extracting features from the training image data, associating similar extracted features with a corresponding task of the plurality of tasks, associating different extracted features with different other tasks of the plurality of tasks, causing the plurality of tasks to generate task-specific predictions of feature characteristics based on the training image data, determining a bias between the task-specific prediction for each task and corresponding task-specific ground truth data, and adjusting parameters of each of the plurality of tasks to cause the bias to meet a pre-defined confidence level; receive image data from an image data collection system associated with an autonomous vehicle; and perform an operational phase comprising extracting features from the image data, causing the plurality of trained tasks to execute concurrently to generate task-specific predictions of feature characteristics based on the image data, and output the task-specific predictions to an autonomous vehicle subsystem of the autonomous vehicle.
 16. The non-transitory machine-useable storage medium of claim 15 wherein the instructions further comprise a neural network.
 17. The non-transitory machine-useable storage medium of claim 15 wherein the instructions are further configured to obtain ground truth data from a manual annotation data collection system, which comprises gathering information from human labelers and automated processes.
 18. The non-transitory machine-useable storage medium of claim 15 wherein the instructions are further configured to receive a plurality of training images gathered from a plurality of perception devices by the training image data collection system, the plurality of perception devices comprising cameras and sensors.
 19. The non-transitory machine-useable storage medium of claim 15 wherein the instructions are further configured to extract features using semantic segmentation.
 20. The non-transitory machine-useable storage medium of claim 15 wherein the plurality of tasks are associated with shared layers among different pixel-level image segmentation. 